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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
Are current AI Conferences being overloaded?
Published:
This post is adapted from a short social media reflection I previously shared about the explosive growth of AI research submissions and the mounting pressure it places on the conference review system, mentioned by a post on Reddit.
From a Tragic Fire in Hanoi to a Larger Question: What Does It Mean to Be Civilized?
Published:
This reflection was inspired by the tragic fire in Hanoi on September 12, 2023.
At around 23:30 ICT (UTC+07:00), a devastating fire broke out in a nine-story mini apartment building. Of the approximately 150 residents, 56 lost their lives and 37 were injured.
How to Prepare a Strong Profile for Doctoral Applications
Published:
This post is a reflection on my own experience applying for PhD opportunities in the United States. I hope it can serve as a practical guide for anyone preparing their profile and application materials for doctoral study, whether in the US or elsewhere.
portfolio
How to Learn English Effectively
Published:
This page gathers my lesson plans, study notes, and recommended materials for learning English in a structured and effective way.
Linear Algebra for Machine Learning and Data Science
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This page collects my projects, notes, and implementations for learning and reviewing the linear algebra foundations behind modern machine learning and data science.
Optimization for Machine Learning and Data Science
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This page gathers my projects and lecture notes on optimization, with a particular focus on concepts and algorithms that are essential in machine learning, data science, and operations research.
Probability and Statistics for Machine Learning and Data Science
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This page collects my lecture notes and learning materials in probability, statistics, and applied statistical modeling for machine learning and data science.
publications
Measurement setup for differential spectral responsivity of solar cells
Published in Optical Review, 2020
We present a setup for measuring differential spectral responsivities of unifacial and bifacial solar cells under bias light conditions.
Recommended citation: Kärhä, P., Baumgartner, H., Askola, J. et al. Measurement setup for differential spectral responsivity of solar cells. Opt Rev 27, 195–204 (2020).
The Lynchpin of In-Memory Computing: A Benchmarking Framework for Vector-Matrix Multiplication in RRAMs
Published in 2024 International Conference on Neuromorphic Systems (ICONS'24), 2024
We introduce MELISO (In-Memory Linear Solver), a comprehensive end-to-end VMM benchmarking framework tailored for RRAM-based systems. MELISO evaluates the error propagation in VMM operations, analyzing the impact of RRAM device metrics on error magnitude and distribution.
Recommended citation: M. T. Rahman Chowdhury, H. Q. Nguyen Vo, P. Ramanan, M. Yildirim and G. Tutuncuoglu, "The Lynchpin of In-Memory Computing: A Benchmarking Framework for Vector-Matrix Multiplication in RRAMs," 2024 International Conference on Neuromorphic Systems (ICONS), Arlington, VA, USA, 2024, pp. 336-342.
SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problems
Published in 2024 IEEE International Conference on Big Data (BigData), 2024
We present SplitVAE, a decentralized scenario generation framework that leverages variational autoencoders to generate high-quality scenarios without moving stakeholder data.
Recommended citation: H. M. M. Islam, H. Q. N. Vo and P. Ramanan, "SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problems", in 2024 IEEE International Conference on Big Data (BigData), Washington, DC, USA, 2024, pp. 938-948.
Harnessing the Full Potential of RRAMs through Scalable and Distributed In-Memory Computing with Integrated Error Correction
Published in arXiv preprint, 2025
We introduce MELISO+: a distributed, full-stack RRAM in-memory computing framework with integrated two-tier error correction for scalable, high-dimensional matrix computations.
Recommended citation: Huynh Q. N. Vo, Md Tawsif Rahman Chowdhury, Paritosh Ramanan, Murat Yildirim, and Gozde Tutuncuoglu. (2025). Harnessing the Full Potential of RRAMs through Scalable and Distributed In-Memory Computing with Integrated Error Correction. arXiv preprint arXiv:2508.13298.
From GPUs to RRAMs: Distributed In-Memory Primal-Dual Hybrid Gradient Method for Solving Large-Scale Linear Optimization Problems
Published in 2026 SIAM Conference on Parallel Processing for Scientific Computing (PP26), 2026
We present a distributed primal-dual hybrid gradient (PDHG) method co-designed for resistive random-access memory (RRAM) in-memory computing, enabling large-scale linear optimization with dramatically reduced energy and latency compared to GPU baselines.
Recommended citation: Huynh Q. N. Vo, Md Tawsif Rahman Chowdhury, Paritosh Ramanan, Gozde Tutuncuoglu, Junchi Yang, Feng Qiu, and Murat Yildirim. (2026). From GPUs to RRAMs: Distributed In-Memory Primal-Dual Hybrid Gradient Method for Solving Large-Scale Linear Optimization Problems. In: Proceedings of the 2026 SIAM Conference on Parallel Processing for Scientific Computing (PP26).
talks
Deep Learning Models for Fault Detection and Diagnosis in Photovoltaic Modules Manufacture
Published:
The usage of photovoltaic (PV) systems has experienced exponential growth. This growth, however, places gargantuan pressure on the solar energy industry’s manufacturing sector and subsequently begets issues associated with the quality of PV systems, especially the PV module. Currently, fault detection and diagnosis (FDD) are challenging due to many factors including but not limited to requirements of sophisticated measurement instruments and experts. Recent advances in deep learning (DL) have proven its feasibility in image classification and object detection. Thus, DL can be extended to visual fault detection using data generated by electroluminescence (EL) imaging instruments. Here, the authors propose an in-depth approach to exploratory data analysis of EL data and several techniques based on supervised learning to detect and diagnose visual faults and defects presented in a module.
SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problems
Published:
Stochastic optimization problems in large-scale multi-stakeholder networked systems (e.g., power grids and supply chains) rely on data-driven scenarios to encapsulate uncertainties and complex spatiotemporal interdependencies. However, centralized aggregation of stakeholder data is challenging due to privacy, computational, and logistical bottlenecks. In this paper, we present SplitVAEs, a decentralized scenario generation framework that leverages the split learning paradigm and variational autoencoders to generate high-quality scenarios without moving stakeholder data. With the help of large-scale, distributed memory-based experiments, we demonstrate the broad applicability of SplitVAEs in three distinct domain areas: power systems, industrial carbon emissions, and supply chains. Our experiments indicate that SplitVAEs can learn spatial and temporal interdependencies in large-scale networks to generate scenarios that match the joint historical distribution of stakeholder data in a decentralized manner. Our results show that SplitVAEs outperform conventional state-of-the-art methodologies and provide a superior, computationally efficient, and privacy-compliant alternative to scenario generation.
Importance Sampling in Variational Autoencoders to Generate Industrial Carbon Emissions Scenarios
Published:
Concerns about the negative environmental impact of fossil fuels have intensified interest in decarbonization, with renewable energy playing a central role despite substantial uncertainties in supply and demand. Stochastic programming provides a principled framework for decision-making under such uncertainty, but its performance depends critically on the quality of generated scenarios and their associated probability masses. In this talk, we explore the use of variational autoencoders (VAEs), together with decentralized variants and importance-weighted autoencoders (IWAEs), for generating high-fidelity industrial carbon-emissions scenarios. Using industrial-sector CO$_2$ emissions data from Texas oil refineries, we demonstrate that these generative models can preserve key spatial and temporal structures while also supporting the estimation of scenario likelihoods required for stochastic optimization. The results highlight the promise of deep generative models as scalable tools for scenario generation in decarbonization-driven industrial decision-making.
Decentralized Importance Sampling in Variational Autoencoders to Generate Industrial Scenarios
Published:
Decarbonization of industrial systems increasingly relies on stochastic optimization models that require high-quality scenarios to represent uncertainty in renewable energy and process operations. In practice, however, the data needed to construct such scenarios are often siloed across stakeholders and may be heterogeneous, making centralized scenario generation difficult. In this talk, we present a decentralized framework based on variational autoencoders (VAEs), SplitVAEs, and importance-weighted autoencoders (IWAEs) to generate industrial scenarios together with their probability masses for stochastic programming applications. Through experiments on solar and wind energy data in Texas, we show that the proposed decentralized approach retains essential spatiotemporal information, produces scenario quality comparable to centralized methods, and achieves favorable computational performance. These results suggest that decentralized deep generative models offer a practical and privacy-aware pathway for large-scale industrial scenario generation.
From GPUs to RRAMs: Distributed In-Memory Primal-Dual Hybrid Gradient Method for Solving Large-Scale Linear Optimization Problems
Published:
Linear programs are central to operations research and arise in domains such as transportation, logistics, and power systems, where faster solution methods can significantly improve large-scale decision-making. Recent advances in primal-dual hybrid gradient (PDHG) methods have made GPUs a viable platform for large linear optimization problems, but their performance is still constrained by the von Neumann bottleneck, since repeated data movement between memory and processors incurs substantial latency and energy costs. In this talk, we present MELISO: a distributed in-memory computing framework that uses resistive random-access memory (RRAM) devices to accelerate PDHG for large-scale linear optimization. Our approach integrates an RRAM-oriented design framework, the MELISO large-scale distributed simulator, and hardware-aware modifications to PDHG, including matrix encoding strategies, in-memory operator norm estimation, and step-size adaptations suited to noisy analog computation. We further discuss theoretical guarantees for the inexact algorithm under realistic noise assumptions and show that RRAM-based implementations can achieve substantial gains in latency and energy efficiency while maintaining solution quality comparable to GPU-based methods and commercial solvers.
teaching
How to make frames and infer people - A lecture in Optics and Computer Vision
Guest Lecture - Undergraduate Course, RMIT University Vietnam, Department of Electrical Engineering, 2023
In this lecture, we would delve deeply into the intricate anatomy of the human eye, exploring how our understanding of its structure and function has been instrumental in the development of sophisticated cameras. Furthermore, we would examine the key operating principles of cameras that empower us to perform complex computer vision tasks.